Estimating the contribution of setting-specific contacts to SARS-CoV-2 transmission using digital contact tracing data

While many countries employed digital contact tracing to contain the spread of SARS-CoV-2, the contribution of cospace-time interaction (i.e., individuals who shared the same space and time) to transmission and to super-spreading in the real world has seldom been systematically studied due to the lack of systematic sampling and testing of contacts. To address this issue, we utilized data from 2230 cases and 220,878 contacts with detailed epidemiological information during the Omicron outbreak in Beijing in 2022. We observed that contact number per day of tracing for individuals in dwelling, workplace, cospace-time interactions, and community settings could be described by gamma distribution with distinct parameters. Our findings revealed that 38% of traced transmissions occurred through cospace-time interactions whilst control measures were in place. However, using a mathematical model to incorporate contacts in different locations, we found that without control measures, cospace-time interactions contributed to only 11% (95%CI: 10%–12%) of transmissions and the super-spreading risk for this setting was 4% (95%CI: 3%–5%), both the lowest among all settings studied. These results suggest that public health measures should be optimized to achieve a balance between the benefits of digital contact tracing for cospace-time interactions and the challenges posed by contact tracing within the same setting.


Figures S1 to S8
Tables S1 to S6       The impact of social distance policy on contact patterns was assumed to be proportional to intracity mobility.Consequently, we multiplied the mean contacts for workplaces, cospace-time interactions, community settings, and unknown settings by 1.34 (Figure S7).The contact pattern for dwellings was not adjusted since these contacts are essential and less likely to be affected by control measures.No statistics were derived due to the use of a single sample for each bar.
Table S1.The definition of contacts with infected individuals under different locations.

Figure S1 .
Figure S1.The estimated density function for generation time and incubation period.

Figure S2 .
Figure S2.The distribution of contact tracing window defined in our dataset.

Figure S3 .
Figure S3.Quality control for the contact data.

Figure S5 .
Figure S5.Quantile-quantile (Q-Q) plots for the estimated distribution for contact number per day under different locations.

Figure S6 .
Figure S6.Dynamic infectiousness under different locations.The probability of being infected with effective contact under different locations with a case (p is shown as a function of the time since infection τ).Distinct colors represent the median of calculated p for the five different locations.The gray area represents the 95% confidence interval.

Figure S7 .
Figure S7.The comparison of intra-city mobility between 2022 and 2023 in Beijing.The mobility index in 2023 (after China's reopening) was, on average, 1.34 times higher than that in 2022.

Figure S8 .
Figure S8.The transmission contribution and the super-spreading event risk under different locations after adjusting the Dynamic zero COVID policy based on the intra-city mobility.The impact of social distance policy on contact patterns was assumed to be proportional to intracity mobility.Consequently, we multiplied the mean contacts for workplaces, cospace-time interactions, community settings, and unknown settings by 1.34 (FigureS7).The contact pattern for dwellings was not adjusted since these contacts are essential and less likely to be affected by control measures.No statistics were derived due to the use of a single sample for each bar.
interaction meeting the following two conditions: (1) individuals who have been present during the same time period as the case, or within the subsequent three hours, and without direct close contact; (2) individuals who have been in poorly ventilated and confined spaces, particularly those with a per capita area below 1.5 m 2 (such as participating in collective entertainment or fitness endeavors, companions or acquaintances sharing meals at bars or restaurants; social participants sharing the same car-hailing service and individuals sharing elevators).Community settings a contact who lives in the same residential structure, or acts as a neighbor to an individual infected with SARS-CoV-2.Unknown settings special scenarios where it's difficult to determine categories (such as those encountered on crowded streets).

Table S2 .
Summary statistics of SARS-CoV-2 cases in the Omicron outbreak in Beijing.

Table S3 .
Summary statistics of contacts in the Omicron outbreak in Beijing.

Table S4 .
The estimations for incubation period and generation time under different forms of distributions in Beijing during the Spring of 2022, China.

Table S5 .
Comparison between the age demographics in Beijing with the age distribution of cases and contacts in the Omicron outbreak in Beijing.The data presented in this table is consistent with that in Figure 2.

Table S6 .
The sensitivity analysis for the contact number per day of tracing when aligning the cases' age distribution with the age demographics in Beijing.The cases were sub-sampled and fitted to Gamma distribution.The mean and variance were obtained from the fitted Gamma distribution, and this process was repeated for 100 times.The median and 95% confidence interval (CI) were then derived from the sub-sampling results.